# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch


class CosyVoiceModel:
    def __init__(
        self, llm: torch.nn.Module, flow: torch.nn.Module, hift: torch.nn.Module
    ):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.llm = llm
        self.flow = flow
        self.hift = hift

    def load(self, llm_model, flow_model, hift_model):
        self.llm.load_state_dict(
            torch.load(llm_model, weights_only=True, map_location=self.device)
        )
        self.llm.to(self.device).eval()
        self.flow.load_state_dict(
            torch.load(flow_model, weights_only=True, map_location=self.device)
        )
        self.flow.to(self.device).eval()
        self.hift.load_state_dict(
            torch.load(hift_model, weights_only=True, map_location=self.device)
        )
        self.hift.to(self.device).eval()

    def inference(
        self,
        text,
        text_len,
        flow_embedding,
        llm_embedding=torch.zeros(0, 192),
        prompt_text=torch.zeros(1, 0, dtype=torch.int32),
        prompt_text_len=torch.zeros(1, dtype=torch.int32),
        llm_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        llm_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
        flow_prompt_speech_token=torch.zeros(1, 0, dtype=torch.int32),
        flow_prompt_speech_token_len=torch.zeros(1, dtype=torch.int32),
        prompt_speech_feat=torch.zeros(1, 0, 80),
        prompt_speech_feat_len=torch.zeros(1, dtype=torch.int32),
    ):
        tts_speech_token = self.llm.inference(
            text=text.to(self.device),
            text_len=text_len.to(self.device),
            prompt_text=prompt_text.to(self.device),
            prompt_text_len=prompt_text_len.to(self.device),
            prompt_speech_token=llm_prompt_speech_token.to(self.device),
            prompt_speech_token_len=llm_prompt_speech_token_len.to(self.device),
            embedding=llm_embedding.to(self.device),
            beam_size=1,
            sampling=25,
            max_token_text_ratio=30,
            min_token_text_ratio=3,
        )
        tts_mel = self.flow.inference(
            token=tts_speech_token,
            token_len=torch.tensor([tts_speech_token.size(1)], dtype=torch.int32).to(
                self.device
            ),
            prompt_token=flow_prompt_speech_token.to(self.device),
            prompt_token_len=flow_prompt_speech_token_len.to(self.device),
            prompt_feat=prompt_speech_feat.to(self.device),
            prompt_feat_len=prompt_speech_feat_len.to(self.device),
            embedding=flow_embedding.to(self.device),
        )
        tts_speech = self.hift.inference(mel=tts_mel).cpu()
        torch.cuda.empty_cache()
        return {"tts_speech": tts_speech}
